Overview

Dataset statistics

Number of variables14
Number of observations2773
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory303.4 KiB
Average record size in memory112.0 B

Variable types

Numeric14

Alerts

gross_revenue is highly correlated with invoice_no and 4 other fieldsHigh correlation
invoice_no is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_quantity is highly correlated with avg_ticket and 2 other fieldsHigh correlation
total_quantity is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_ticket is highly correlated with avg_quantity and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with df_indexHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 5 other fieldsHigh correlation
avg_un_basket_size is highly correlated with avg_quantity and 2 other fieldsHigh correlation
stock_code is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qty_returns is highly correlated with gross_revenue and 6 other fieldsHigh correlation
df_index is highly correlated with avg_recency_daysHigh correlation
avg_ticket is highly skewed (γ1 = 27.67812665) Skewed
frequency is highly skewed (γ1 = 46.07732187) Skewed
qty_returns is highly skewed (γ1 = 21.6260127) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 33 (1.2%) zeros Zeros
qty_returns has 1481 (53.4%) zeros Zeros

Reproduction

Analysis started2022-10-21 22:35:24.238604
Analysis finished2022-10-21 22:35:59.420288
Duration35.18 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct2773
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2250.579517
Minimum0
Maximum5695
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:35:59.569293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile181.6
Q1901
median2061
Q33411
95-th percentile4958.4
Maximum5695
Range5695
Interquartile range (IQR)2510

Descriptive statistics

Standard deviation1526.372877
Coefficient of variation (CV)0.6782132625
Kurtosis-0.9560471528
Mean2250.579517
Median Absolute Deviation (MAD)1241
Skewness0.3799089199
Sum6240857
Variance2329814.159
MonotonicityStrictly increasing
2022-10-21T19:35:59.727284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
29101
 
< 0.1%
28961
 
< 0.1%
28971
 
< 0.1%
29001
 
< 0.1%
29011
 
< 0.1%
29051
 
< 0.1%
29061
 
< 0.1%
29071
 
< 0.1%
29081
 
< 0.1%
Other values (2763)2763
99.6%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
56951
< 0.1%
56851
< 0.1%
56791
< 0.1%
56541
< 0.1%
56481
< 0.1%
56371
< 0.1%
56361
< 0.1%
56201
< 0.1%
56191
< 0.1%
56101
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2773
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15285.28128
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:35:59.887286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12626.6
Q113815
median15241
Q316780
95-th percentile17950.4
Maximum18287
Range5940
Interquartile range (IQR)2965

Descriptive statistics

Standard deviation1715.152588
Coefficient of variation (CV)0.1122094226
Kurtosis-1.207029283
Mean15285.28128
Median Absolute Deviation (MAD)1484
Skewness0.0166125065
Sum42386085
Variance2941748.399
MonotonicityNot monotonic
2022-10-21T19:36:00.036284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
144821
 
< 0.1%
170581
 
< 0.1%
177041
 
< 0.1%
169331
 
< 0.1%
137721
 
< 0.1%
162491
 
< 0.1%
141981
 
< 0.1%
139891
 
< 0.1%
179301
 
< 0.1%
Other values (2763)2763
99.6%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182651
< 0.1%
182631
< 0.1%
182611
< 0.1%
182601
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2759
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2845.044446
Minimum36.56
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:36:00.192320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum36.56
5-th percentile264.548
Q1628.78
median1169.94
Q32424.04
95-th percentile7490.982
Maximum279138.02
Range279101.46
Interquartile range (IQR)1795.26

Descriptive statistics

Standard deviation10466.82835
Coefficient of variation (CV)3.678968308
Kurtosis372.786099
Mean2845.044446
Median Absolute Deviation (MAD)687.93
Skewness17.09734698
Sum7889308.25
Variance109554495.8
MonotonicityNot monotonic
2022-10-21T19:36:00.334317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025.442
 
0.1%
745.062
 
0.1%
889.932
 
0.1%
734.942
 
0.1%
3312
 
0.1%
1314.452
 
0.1%
379.652
 
0.1%
1353.742
 
0.1%
598.22
 
0.1%
1078.962
 
0.1%
Other values (2749)2753
99.3%
ValueCountFrequency (%)
36.561
< 0.1%
521
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
70.021
< 0.1%
77.41
< 0.1%
84.651
< 0.1%
90.31
< 0.1%
93.351
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65039.621
< 0.1%

recency_days
Real number (ℝ≥0)

ZEROS

Distinct252
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.64731338
Minimum0
Maximum372
Zeros33
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:36:00.487291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median29
Q373
95-th percentile211
Maximum372
Range372
Interquartile range (IQR)63

Descriptive statistics

Standard deviation68.42352582
Coefficient of variation (CV)1.207886513
Kurtosis3.430442793
Mean56.64731338
Median Absolute Deviation (MAD)23
Skewness1.898015296
Sum157083
Variance4681.778886
MonotonicityNot monotonic
2022-10-21T19:36:00.740285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.6%
487
 
3.1%
285
 
3.1%
385
 
3.1%
876
 
2.7%
1067
 
2.4%
966
 
2.4%
765
 
2.3%
1762
 
2.2%
2255
 
2.0%
Other values (242)2026
73.1%
ValueCountFrequency (%)
033
 
1.2%
199
3.6%
285
3.1%
385
3.1%
487
3.1%
543
1.6%
765
2.3%
876
2.7%
966
2.4%
1067
2.4%
ValueCountFrequency (%)
3721
 
< 0.1%
3661
 
< 0.1%
3601
 
< 0.1%
3583
0.1%
3541
 
< 0.1%
3371
 
< 0.1%
3362
0.1%
3341
 
< 0.1%
3332
0.1%
3301
 
< 0.1%

invoice_no
Real number (ℝ≥0)

HIGH CORRELATION

Distinct55
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.054814281
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:36:00.922322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.072771138
Coefficient of variation (CV)1.498439212
Kurtosis183.9078799
Mean6.054814281
Median Absolute Deviation (MAD)2
Skewness10.62384214
Sum16790
Variance82.31517613
MonotonicityNot monotonic
2022-10-21T19:36:01.091290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2779
28.1%
3499
18.0%
4393
14.2%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
Other values (45)278
 
10.0%
ValueCountFrequency (%)
2779
28.1%
3499
18.0%
4393
14.2%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

avg_quantity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2599
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.10229044
Minimum1
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:36:01.258320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.559069246
Q16.364583333
median10.33333333
Q315.02797203
95-th percentile47.43011236
Maximum2000
Range1999
Interquartile range (IQR)8.663388695

Descriptive statistics

Standard deviation76.47130626
Coefficient of variation (CV)3.804109112
Kurtosis385.7554308
Mean20.10229044
Median Absolute Deviation (MAD)4.266666667
Skewness17.64458645
Sum55743.6514
Variance5847.860681
MonotonicityNot monotonic
2022-10-21T19:36:01.411289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108
 
0.3%
9.3333333337
 
0.3%
116
 
0.2%
10.44
 
0.1%
184
 
0.1%
124
 
0.1%
12.54
 
0.1%
6.54
 
0.1%
94
 
0.1%
23.333333333
 
0.1%
Other values (2589)2725
98.3%
ValueCountFrequency (%)
11
< 0.1%
1.0526315791
< 0.1%
1.0555555561
< 0.1%
1.1315789471
< 0.1%
1.218751
< 0.1%
1.2571428571
< 0.1%
1.2604166671
< 0.1%
1.281
< 0.1%
1.3761467891
< 0.1%
1.394088671
< 0.1%
ValueCountFrequency (%)
20001
< 0.1%
1802.81
< 0.1%
1756.51
< 0.1%
1009.51
< 0.1%
7401
< 0.1%
715.21
< 0.1%
664.61538461
< 0.1%
6571
< 0.1%
6001
< 0.1%
541.88888891
< 0.1%

total_quantity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1638
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1671.783988
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:36:01.576320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile119.6
Q1330
median704
Q31478
95-th percentile4614
Maximum196844
Range196842
Interquartile range (IQR)1148

Descriptive statistics

Standard deviation5890.699162
Coefficient of variation (CV)3.523600658
Kurtosis485.4627718
Mean1671.783988
Median Absolute Deviation (MAD)452
Skewness18.17697463
Sum4635857
Variance34700336.61
MonotonicityNot monotonic
2022-10-21T19:36:01.731321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
1508
 
0.3%
2468
 
0.3%
2007
 
0.3%
2607
 
0.3%
12007
 
0.3%
3007
 
0.3%
3947
 
0.3%
4937
 
0.3%
5167
 
0.3%
Other values (1628)2697
97.3%
ValueCountFrequency (%)
21
< 0.1%
161
< 0.1%
171
< 0.1%
191
< 0.1%
201
< 0.1%
251
< 0.1%
272
0.1%
301
< 0.1%
321
< 0.1%
332
0.1%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%
502551
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2771
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.10411414
Minimum2.150588235
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:36:01.892284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.852453809
Q112.41668
median17.94081081
Q325.02555556
95-th percentile87.75747368
Maximum4453.43
Range4451.279412
Interquartile range (IQR)12.60887556

Descriptive statistics

Standard deviation107.6316856
Coefficient of variation (CV)3.352582324
Kurtosis1054.619311
Mean32.10411414
Median Absolute Deviation (MAD)6.337289189
Skewness27.67812665
Sum89024.70852
Variance11584.57974
MonotonicityNot monotonic
2022-10-21T19:36:02.033289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.478333332
 
0.1%
4.1622
 
0.1%
25.67611941
 
< 0.1%
44.955641031
 
< 0.1%
32.597751
 
< 0.1%
19.030483871
 
< 0.1%
28.554516131
 
< 0.1%
12.800681821
 
< 0.1%
6.3962146891
 
< 0.1%
26.087971011
 
< 0.1%
Other values (2761)2761
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
4453.431
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%
602.45313231
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION

Distinct305
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.50126217
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:36:02.176321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median59
Q399
95-th percentile224
Maximum366
Range365
Interquartile range (IQR)65

Descriptive statistics

Standard deviation66.57709146
Coefficient of variation (CV)0.8481021785
Kurtosis3.683144872
Mean78.50126217
Median Absolute Deviation (MAD)30
Skewness1.83060598
Sum217684
Variance4432.509107
MonotonicityNot monotonic
2022-10-21T19:36:02.319300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3540
 
1.4%
7039
 
1.4%
5536
 
1.3%
2136
 
1.3%
3136
 
1.3%
4536
 
1.3%
2535
 
1.3%
4634
 
1.2%
3833
 
1.2%
2633
 
1.2%
Other values (295)2415
87.1%
ValueCountFrequency (%)
19
0.3%
25
 
0.2%
38
0.3%
48
0.3%
55
 
0.2%
68
0.3%
712
0.4%
812
0.4%
910
0.4%
1018
0.6%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3641
 
< 0.1%
3631
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04971312176
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:36:02.472320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008746355685
Q10.01578947368
median0.0243902439
Q30.04166666667
95-th percentile0.1153846154
Maximum17
Range16.99455041
Interquartile range (IQR)0.02587719298

Descriptive statistics

Standard deviation0.3376551076
Coefficient of variation (CV)6.792072107
Kurtosis2295.704265
Mean0.04971312176
Median Absolute Deviation (MAD)0.01069161377
Skewness46.07732187
Sum137.8544866
Variance0.1140109717
MonotonicityNot monotonic
2022-10-21T19:36:02.628285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.062518
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.6%
0.0833333333315
 
0.5%
0.0909090909115
 
0.5%
0.0294117647114
 
0.5%
0.0344827586214
 
0.5%
0.0192307692313
 
0.5%
0.0256410256413
 
0.5%
0.0212765957413
 
0.5%
Other values (1215)2625
94.7%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%
1.1428571431
 
< 0.1%
18
0.3%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

qty_returns
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct204
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.97367472
Minimum0
Maximum9014
Zeros1481
Zeros (%)53.4%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:36:02.790320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile96.8
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation290.7142899
Coefficient of variation (CV)8.312374729
Kurtosis571.7456843
Mean34.97367472
Median Absolute Deviation (MAD)0
Skewness21.6260127
Sum96982
Variance84514.79837
MonotonicityNot monotonic
2022-10-21T19:36:02.936321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
53.4%
1129
 
4.7%
2117
 
4.2%
382
 
3.0%
472
 
2.6%
663
 
2.3%
555
 
2.0%
1245
 
1.6%
839
 
1.4%
938
 
1.4%
Other values (194)652
23.5%
ValueCountFrequency (%)
01481
53.4%
1129
 
4.7%
2117
 
4.2%
382
 
3.0%
472
 
2.6%
555
 
2.0%
663
 
2.3%
738
 
1.4%
839
 
1.4%
938
 
1.4%
ValueCountFrequency (%)
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1937
Distinct (%)69.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231.446111
Minimum1
Maximum6009.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:36:03.077322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45
Q1103.3333333
median172
Q3278.2
95-th percentile585.7
Maximum6009.333333
Range6008.333333
Interquartile range (IQR)174.8666667

Descriptive statistics

Standard deviation261.7394008
Coefficient of variation (CV)1.130887012
Kurtosis115.4829522
Mean231.446111
Median Absolute Deviation (MAD)81
Skewness7.715252816
Sum641800.0657
Variance68507.51392
MonotonicityNot monotonic
2022-10-21T19:36:03.216339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
869
 
0.3%
608
 
0.3%
758
 
0.3%
2087
 
0.3%
1057
 
0.3%
737
 
0.3%
1367
 
0.3%
827
 
0.3%
1977
 
0.3%
Other values (1927)2695
97.2%
ValueCountFrequency (%)
11
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
11.8751
< 0.1%
ValueCountFrequency (%)
6009.3333331
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%
2082.2258061
< 0.1%
20001
< 0.1%
1903.51
< 0.1%
1866.9333331
< 0.1%

avg_un_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct897
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.14109332
Minimum0.2
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:36:03.364325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.545454545
median13.5
Q322
95-th percentile45.1
Maximum177
Range176.8
Interquartile range (IQR)14.45454545

Descriptive statistics

Standard deviation14.26277434
Coefficient of variation (CV)0.832080782
Kurtosis10.00830576
Mean17.14109332
Median Absolute Deviation (MAD)6.666666667
Skewness2.24639468
Sum47532.25179
Variance203.4267318
MonotonicityNot monotonic
2022-10-21T19:36:03.509323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
834
 
1.2%
1333
 
1.2%
932
 
1.2%
1632
 
1.2%
732
 
1.2%
1230
 
1.1%
1429
 
1.0%
629
 
1.0%
1729
 
1.0%
18.529
 
1.0%
Other values (887)2464
88.9%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
1771
< 0.1%
1051
< 0.1%
1041
< 0.1%
981
< 0.1%
95.51
< 0.1%
94.333333331
< 0.1%
93.333333331
< 0.1%
89.6251
< 0.1%
871
< 0.1%
85.666666671
< 0.1%

stock_code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct467
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.7890371
Minimum2
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-10-21T19:36:03.666289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q134
median72
Q3143
95-th percentile400.2
Maximum7838
Range7836
Interquartile range (IQR)109

Descriptive statistics

Standard deviation277.8250768
Coefficient of variation (CV)2.140589706
Kurtosis336.7416641
Mean129.7890371
Median Absolute Deviation (MAD)45
Skewness15.34716552
Sum359905
Variance77186.7733
MonotonicityNot monotonic
2022-10-21T19:36:03.810320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2838
 
1.4%
3534
 
1.2%
2730
 
1.1%
2630
 
1.1%
2930
 
1.1%
1527
 
1.0%
1927
 
1.0%
2527
 
1.0%
3127
 
1.0%
3326
 
0.9%
Other values (457)2477
89.3%
ValueCountFrequency (%)
211
0.4%
312
0.4%
416
0.6%
516
0.6%
624
0.9%
714
0.5%
813
0.5%
919
0.7%
1019
0.7%
1123
0.8%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

Interactions

2022-10-21T19:35:56.797774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:32.194624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:33.968244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:35.887913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:37.788952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:39.790925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:41.736923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:43.620916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:45.718925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:47.471687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:49.356072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:51.356081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:53.084075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:54.926775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:56.924776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:32.362408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:34.090204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:36.007921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:37.919922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:39.929927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:41.865952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:43.753924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:45.836915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:47.600685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:49.487073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:51.467077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:53.226072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:55.050815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:57.057777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:32.478426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:34.222204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:36.133958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:38.053949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:40.082922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:41.999953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:43.886929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:45.958970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:47.730669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:49.627084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:51.581108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:53.347109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:55.180785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:57.188776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:32.589408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:34.454242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:36.269916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:38.181915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:40.220914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:42.127927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:44.169960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:46.070916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:47.869649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:49.759123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:51.697075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:53.473085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:55.317776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:57.327811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:32.707472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:34.589202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:36.411918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:38.316916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:40.362926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:42.265965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:44.310960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:46.196964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:47.997647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:49.893087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:51.819113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:53.608074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:55.450774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:57.468782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:32.832466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:34.724959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:36.556916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:38.467925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:40.516914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:42.405957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:44.452958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:46.325916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:48.133679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:50.051116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:51.943114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:53.744116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:55.588782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:57.614774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:32.966463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:34.861914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:36.691925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:38.602951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:40.651948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:42.552970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:44.604965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:46.452958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:48.266678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:50.195107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:52.072078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:53.881111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:55.725781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:57.756773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:33.097472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:35.002959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:36.830930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:38.871969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:40.790914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:42.701957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:44.760966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:46.584951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:48.402683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:50.324104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:52.201118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:54.023077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:55.865774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:57.876774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:33.205495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:35.117948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:36.953954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:38.995917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:40.914925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:42.822963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:44.888968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:46.704921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:48.527686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:50.440077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:52.319112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:54.150071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:55.987777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:58.010776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:33.329470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:35.258916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:37.104931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:39.126916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:41.051963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:42.962917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:45.029958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:46.845934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:48.667645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:50.740073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:52.452109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:54.284775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:56.127775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:58.144775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:33.457460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:35.386953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:37.242955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:39.259924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:41.187953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:43.096915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:45.168954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:46.967687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:48.804685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:50.865074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:52.580082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:54.412774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:56.264775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:58.266777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:33.579203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:35.505921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:37.370922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:39.386924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:41.313951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:43.215952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:45.290915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:47.082681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:48.936648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:50.981073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:52.696112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:54.531775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:56.392775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:58.626775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:33.701203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:35.630931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:37.506925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:39.521921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:41.446930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:43.347928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:45.421921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:47.203682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:49.070649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:51.104072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:52.822133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:54.659775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:56.527810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:58.760775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:33.828202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:35.759922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:37.647917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:39.651928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:41.597920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:43.479928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:45.563926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:47.330687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:49.204684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:51.224072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:52.951076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:54.791774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-21T19:35:56.662810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-10-21T19:36:03.955289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-21T19:36:04.182292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-21T19:36:04.401321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-21T19:36:04.624323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-21T19:35:58.986782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-21T19:35:59.275812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysinvoice_noavg_quantitytotal_quantityavg_ticketavg_recency_daysfrequencyqty_returnsavg_basket_sizeavg_un_basket_sizestock_code
00178505391.21372.034.05.8350171733.018.1522221.017.00000040.050.9705880.617647297.0
11130473232.5956.09.08.1286551390.018.90403552.00.02830235.0154.44444411.666667171.0
22125836705.382.015.021.6724145028.028.90250026.00.04032350.0335.2000007.600000232.0
3313748948.2595.05.015.678571439.033.86607192.00.0179210.087.8000004.80000028.0
4415100876.00333.03.026.66666780.0292.00000020.00.07317122.026.6666670.3333333.0
55152914623.3025.014.020.6078432102.045.32647126.00.04011529.0150.1428574.357143102.0
66146885630.877.021.011.0733943621.017.21978619.00.057221399.0172.4285717.047619327.0
77178095411.9116.012.033.7213112057.088.71983639.00.03352041.0171.4166673.83333361.0
881531160767.900.091.016.05464538194.025.5434644.00.243316474.0419.7142866.2307692379.0
99160982005.6387.07.09.149254613.029.93477647.00.0243900.087.5714294.85714367.0

Last rows

df_indexcustomer_idgross_revenuerecency_daysinvoice_noavg_quantitytotal_quantityavg_ticketavg_recency_daysfrequencyqty_returnsavg_basket_sizeavg_un_basket_sizestock_code
2763561017290525.243.02.03.960784404.05.14941213.00.1428570.0202.00000046.000000102.0
276456191478577.4010.02.028.00000084.025.8000005.00.3333330.042.0000001.0000003.0
2765562017254272.444.02.02.250000252.02.43250011.00.1666670.0126.00000050.000000112.0
2766563617232421.522.02.05.638889203.011.70888912.00.1538460.0101.50000015.00000036.0
2767563717468137.0010.02.023.200000116.027.4000004.00.4000000.058.0000002.5000005.0
2768564813596697.045.02.02.445783406.04.1990367.00.2500000.0203.00000066.500000166.0
27695654148931237.859.02.010.945205799.016.9568492.00.6666670.0399.50000036.00000073.0
2770567914126706.137.03.033.866667508.047.0753333.00.75000050.0169.3333334.66666715.0
27715685135211092.391.03.01.685057733.02.5112414.00.3000000.0244.333333104.000000435.0
2772569515060301.848.04.02.183333262.02.5153331.02.0000000.065.50000020.000000120.0